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All Journal International Journal of Electrical and Computer Engineering Teknika Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Sinergi JUITA : Jurnal Informatika International conference on Information Technology and Business (ICITB) Jurnal Teknologi Informasi dan Bisnis Pengabdian Masyarakat Darmajaya Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining JURNAL PENDIDIKAN TAMBUSAI Jurnal Informasi dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Aisyah Journal of Informatics and Electrical Engineering Jurnal Sains dan Teknologi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer Networks, Architecture and High Performance Computing Yumary: Jurnal Pengabdian kepada Masyarakat Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Jurnal Kedaymas JURNAL ILMIAH GLOBAL EDUCATION Jurnal Teknologi Sistem Informasi Jurnal MathEducation Nusantara Jurnal Publika Pengabdian Masyarakat Journal of Technology Research in Information System and Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science ROUTERS: Jurnal Sistem dan Teknologi Informasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Jurnal Esensi Infokom : Jurnal Esensi Sistem Komputer dan Informasi
Sri Lestari
Institut Informatika Dan Bisnis Darmajaya
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Perbandingan Simple Additive Weighting dan Weighted Product Pada Penerimaan Bantuan Raskin Albarqi, Achmadi Hudadin; Rosman, Firdaus; Lestari, Sri; Fauzi, Chairani
Jurnal Teknologi Sistem Informasi Vol 5 No 1 (2024): Jurnal Teknologi Sistem Informasi
Publisher : Program Studi Sistem Informasi, Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jtsi.v5i1.6997

Abstract

In an effort to reduce the burden on family budgets, the government implemented the Poor Rice Program (Raskin), a social security program for the poor that offers subsidized rice assistance to low-income households. However, in practice, many households are categorized as poor even though they should not be. On the other hand, poor families should be involved, but are not included, and the process is still seen as less focused. Errors in data processing procedures can occur because Raskin recipients are still determined by human data processing. Data processing is time consuming, especially when it comes to ranking and decision making. In this regard, this research explores ways in which Decision Support Systems (DSS) can encourage faster and more precise decision making. Simple Additive Weighting (SAW) and Weighted Product (WP) are the two main methods covered. To increase the effectiveness and accuracy of government aid programs in Indonesia, the main objective of this research is to compare the use of SAW and WP techniques in making decisions regarding the receipt of Rice Miskin aid. The findings of this research support each other by referring to local residents with the pseudonyms W3, W12, W9 , W11, and W17 as recipients of rice assistance from regional governments who are inadequate in decision-making authority positions. Keywords: Decision Support System(DSS),Raskin, Simple Additive Weighting, Weighted Product.
Penerapan Algoritma C.45 Pada Klasifikasi Status Gizi Balita di Posyandu Desa Sukalilah Cibatu Kabupaten Garut Jawa Barat Lestari, Sri; Runi Amanda Amalia
Jurnal Sains dan Teknologi Vol. 5 No. 1 (2023): Jurnal Sains dan Teknologi
Publisher : CV. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pengukuran status gizi balita dapat dinilai dengan mengukur tubuh manusia yang dikenal dengan istilah “Antropometri”. Posyandu di Desa Sukalilah Cibatu Kabupaten Garut Jawa Barat salah satunya yang dapat melakukan pemeriksaan dan pengukuran antropometri agar dapat mengetahui status gizi balita dengan cara orang tua berkonsultasi dengan kader atau petugas kesehatan. Standar rujukan yang dipakai untuk penentuan klasifikasi status gizi dengan antropometri berdasarkan Peraturan Menteri Kesehatan Republik Indonesia Nomor 2 Tahun 2020 tentang Standar Antropometri. Untuk mengukur antropometri disini menggunakan indeks BB/U, karena dapat lebih mudah dimengerti oleh masyarakat umum dan sensitif untuk dapat melihat status gizi jangka pendek Metode yang digunakan pada penelitian ini yaitu dengan menggunakan metode klasifikasi Data Mining algoritma C4.5. Tujuan penelitian untuk mengetahui faktor-faktor apa saja yang mempengaruhi status gizi tersebut dan mendapatkan hasil status gizi agar lebih akurat dan efisien. Hasil akurasi yang diperoleh dari aplikasi rapidminer dengan status gizi kategori baik dan buruk yaitu 95,50%.
Comparison of Performance of K-Nearest Neighbors and Neural Network Algorithm in Bitcoin Price Prediction Apriadi, Eko Aziz; Sriyanto; Lestari, Sri; Yusuf Irianto, Suhendro
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13418

Abstract

This research evaluates and compares the performance of two prediction methods, namely K-Nearest Neighbors (K-NN) and Neural Network, in the context of Bitcoin price prediction. Historical Bitcoin price data is used as input to train and test both algorithms. Experimental results show that the K-NN algorithm produces a Root Mean Square Error (RSME) of 389,770 and a Mean Absolute Error (MAE) of 89,261, while the Neural Network has an RSME of 614,825 and an MAE of 284,190. Performance comparison analysis shows that, on this dataset, K-NN has better performance in predicting Bitcoin prices compared to Neural Network. These findings provide important insights for the selection of crypto asset price prediction models, especially Bitcoin, in financial and investment environments
PENGEMBANGAN MEDIA PEMBELAJARAN MATEMATIKA BERBASIS MACROMEDIA FLASH 8 UNTUK SISWA KELAS VII SMP Ginting, Aurora Riseria Br; Lestari, Sri
Jurnal MathEducation Nusantara Vol 7, No 2 (2024): July 2024
Publisher : Universitas Muslim Nusantara Al Washliyah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32696/jmn.v7i2.390

Abstract

Penelitian ini bertujuan untuk memperoleh media pembelajaran berbasis Macromedia Flash 8 yang valid, praktis, dan efektif sehingga dapat membantu siswa dalam memahami materi segiempat. Jenis penelitian ini merupakan Research and Development (R&D) yang terdiri dari 3 tahap pengembangan yaitu pendefenisian (Define), perancangan (Design), dan pengembangan (Develop). Subjek pada penelitian  R& D ini adalah siswa kelas VII SMP Negeri 1 Atap 4 Barusjahe berjumlah 20 orang. Objek dalam penelitian ini adalah media pembelajaran matematika berbasis Macromedia Flash 8 pada materi segiempat. Instrument yang digunakan pada penelitian ini adalah lembar validasi materi dan media, pre-test dan ketuntasan belajar siswa, serta lembar angket respon dan guru. Seluruh instrument divalidasi  oleh validator dan diuji coba lapangan. Teknik analisis data dengan analisis deskriptif, data yang dikumpulkan dalam penelitian ini adalah hasil validitas media dan materi, analisis data kepraktisan dan keefektifan media. Hasil penelitian pengembangan media pembelajaran matematika berbasis macromedia flash 8 menunjukkan bahwa: (1) Media pembelajarn yang dikembangkan telah memenuhi nilai persentase kevalidan yaitu 87,77% dan 90% dengan kategori sangat valid; (2) Media yang dikembangkan telah memenuhi nilai kepraktisan yaitu (84% dan 87,16%) dengan kategori sangat praktis; (3) Media yang dikembangkan memenuhi kriteria efektif dengan ketuntasan hasil belajar siswa mencapai 85% dan respon positif yang diberikan siswa mencapai 85,5% dari jumlah total siswa.Kata kunci: Macromedia flash 8, Pengembangan Media, Segiempat
Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques Lestari, Sri; Yulmaini, Yulmaini; Aswin, Aswin; Ma'ruf, Singgih Yulizar; Sulyono, Sulyono; Fikri, Ruki Rizal Nul
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3220-3229

Abstract

Recommendation systems are often implemented in e-commerce and micro, small, and medium enterprises (MSMEs) marketplaces to improve consumer services by providing product recommendations according to their interests. However, it still faces problems, namely sparsity and cold start, thus affecting the quality of recommendations. This research proposes clustering and imputation techniques to overcome this problem. The clustering technique used is k-means, while the missing value imputation method uses average values. The imputation results are then implemented in the k-nearest neighbor (KNN) and naïve Bayes algorithms and evaluated based on performance accuracy. Experimental results show an increase in accuracy of 16.48% in the KNN algorithm from 83.52% to 100%. Meanwhile, the naïve Bayes algorithm increased accuracy by 35.30% from 64.70% to 100%.
Analisis Sentimen Publik Terhadap Penurunan Jumlah Pernikahan di Indonesia menggunakan Algoritma K-Nearest Neighbors (KNN) Lailany, Afyra Ar’bah; Lestari, Sri
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.1000

Abstract

Marriage in Indonesia is an important aspect of its culture and society. However, in recent years, there has been a significant decline in the number of marriages in the country. To understand people's views on this phenomenon, this research aims to conduct a sentiment analysis regarding the decline in the number of marriages in Indonesia. A decrease in the number of marriages can cause various economic problems. There are certain factors that contribute to this change, such as shifts in values or economic factors. Knowing views on this matter is important for formulating appropriate policies and finding effective solutions. The method used in this research is analysis using the K-Nearest Neighbors (KNN) algorithm. Data will be collected through comments on Tiktok social media pages. The opinion data obtained will be processed using the KNN algorithm to classify positive, negative or neutral sentiment regarding the decline in the number of marriages. The data used amounted to 2,000 data, and were processed using the CRISP-DM method.
Perbandingan Algoritma Data Mining Untuk Penentuan Lokasi Promosi Penerimaan Mahasiswa Baru Pada IIB Darmajaya Lampung Toro, Robby; Lestari, Sri
Techno.Com Vol. 22 No. 1 (2023): Februari 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i1.7118

Abstract

Tingkat pesaing perguruan tinggi setiap tahunnya mengalami peningkatan yang sangat pesat. Dalam proses mendapatkan mahasiswa baru setiap perguruan tinggi memiliki strategi promosi yang berbeda-beda, seiring perkembangan teknologi maka pihak perguruan tinggi dapat memanfaatkan data mining dalam upaya promosi. Data mining dapat menjadi sebuah dasar atau pedoman untuk menentukan kebijakan  bisnis dalam upaya peningkatan pesaing bisnis perusahaan. Dalam upaya penghematan biaya promosi maka dilakukan pemetaan lokasi promosi. Penelitian ini membandingkan tiga alogoritma klasifikasi agar mendapatkan nilai akurasi yang tinggi sehingga informasi yang dihasilkan lebih akurat. Pemetaan lokasi promosi menggunakan algoritma klasifikasi yaitu, decision tree, naïve bayes, dan k-nearest nighbour. Data set yang digunakan berjumlah 1281 record, dan menggunakan variabel, nama, total, alumni, luar kota, dan asal sekolah, denganlabel yaitu, sangat potensi, potensi, dan kurang potensi. Hasil klasifikasi dapat digunakan pihak pemasaran untuk menentukan lokasi promosi pada tahun yang akan datang. Berdasarkan hasil evaluasi crosss validation dari ketiga alogoritma tersebut maka alogoritma yang memiliki nilai akurasi tertinggi yaitu decision tree sebesar 100%, selanjutnya adalah algoritma k-nearest nighbour sebesar 99,61%, dan yang terakhir algoritma naïve bayes hanya 84,78%.
Aplikasi Pengelolaan Data Profil dan Konsentrasi Dosen Wibowo, Hariyanto; Sulyono, Sulyono; Lestari, Sri; Miranda, Thesa
TEKNIKA Vol. 14 No. 2 (2020): Teknika Juli - Desember 2020
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.13362499

Abstract

The success of the education process is strongly influenced by the quality of human resources, in this case lecturers as educators. While each lecturer has the potential, expertise, qualifications, competency profile and experience that sometimes varies between one lecturer and another. Therefore the management of profile and concentration data is very much needed so that the department can know with certainty the abilities possessed by the lecturer. In the Department of Informatics the management of lecturer profile and concentration data has not been well managed. so this research proposes the Application of Data Management Profile and Concentration of Lecturers in the Informatics Engineering Department IIB Darmajaya by using a system development method that is prototype. The results of this study are expected to be utilized appropriately for policy making, both in terms of determining lecturers, lecturers, and determining membership peer group research, and also for filling accreditation form data and for display on the web of the Department of Informatics.
Assessment Clusterization Teacher Performance with K-Means Algorithm Clustering and Agglomerative Hierarchical Clustering (AHC) Rodiatun, Rodiatun; Lestari, Sri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14200

Abstract

Research This aims to do clustering evaluation teacher performance with the application of the K-means clustering algorithm and agglomerative hierarchical clustering (AHC). Background study This is based on needs to increase quality teaching through analysis and evaluation and better teacher performance. The methods applied involving assessment data collection performance from teachers in the environment education local, processed using a second algorithm The results of the research show that the silhouette score value for K-means reached 0.364, while AHC produced a value 0.343. With Thus, K-means is proven more effective in grouping assessment data and teacher performance compared to AHC. The conclusion of the study This confirms the importance of implementation of the K-means algorithm to get more insight into good evaluation teacher performance. Author Ready to do repairs or revisions to the manuscript. This is in accordance with comments and suggestions from the reviewer as a condition beginning. For processing more, carry on.
Optimizing PSO for classification: comparison of Naïve Bayes and C4.5 for osteoporosis prediction Anugerahwati, Zulfi; Lestari, Sri
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.011

Abstract

Osteoporosis is a medical disease marked by a reduction in bone density, which significantly increases the risk of fractures. Osteoporosis patients do not always exhibit symptoms and because current diagnostic techniques have limitations, early detection is frequently needed. The osteoporosis dataset consists of 1.958 records each containing 15 regular attributes and 1 special attribute as the label.  The attribute represented as “1” for the presence of osteoporosis and “0” for its absence. The primary objective is to predict an individual’s risk of developing osteoporosis, including age, gender, bone density, lifestyle factor, medical history, and nutritional intake of calcium and vitamin D. To achieve this, Naïve Bayes and C4.5 has been employed. PSO is employed to identify the most relevant features, thereby optimizing the efficiency and accuracy of the classification models. The initial step in data preprocessing involved handling missing values to ensure data integrity. After implementing PSO, Naïve bayes improved from 82,65% to 83,67%, while C4.5 exhibited an even greater increase, rising from 91,07% to 96,17%. PSO significantly optimizes model, with the most improvement in C4.5. PSO proves to be a valuable tool for feature selection. Age and Hormonal Change emerged as important for both models. Furthermore, Physical Activity and Calcium Intake, which despite having varying levels of influence, were consistently considered relevant.  By focusing on these significant attributes, enables us more effectively monitor and recognize early signs of osteoporosis. Identifying individuals at high risk, more effective early detection and intervention, improving the potential for timely management and prevention.
Co-Authors Ableo Swares Achmadi Hudadin Albarqi Ade Moussadecq Adigue, Andrea P. Aisha Nurwanti, Lathifah Albarqi, Achmadi Hudadin Amalyanda Azhari Anas Ikhsanudin Andien Amalia Anugerahwati, Zulfi Apri Triansyah Aprilia, Indri Mada ARI SUSANTO Aron Naldi Rirongga Aryono Prihandito Aswin Aswin Aswin Aswin Aswin Aswin Aziz, RZ. Abdul Chairani Chairani Christian Petrus Silalahi Dadang Mulyadi Saleh Deka Mario Dewi Tryanasari Diantoro, Wawan Dwi Ernawati Fatonah Fatonah Faturochman Faturochman Faurani I Santi Singagerda Fauzi, Chairani Febri Arianto Febri Arianto Ferdiansyah, Mohamad Fida Chasanatun, Fida Fikri, Ruki Rizal Nul Firdaus Rosman, Firdaus Fitri Agustina Fitria Fitria - Fitria Fitria Frasatya, {Ariffinto Ginting, Aurora Riseria Br Hadi Nurma Dwi Saputra Hari Sabita Hariyanto Wibowo, Hariyanto Hartono Hartono Hary Sulistyo Hastuti, Zulia Hendra Darmawan Hendra Kurniawan Heny Kusuma Widyaningrum Hovi Herawadi Sudibyo I Komang Swandika Indah Pratiwi Indianto S, Dimas Ismail , Ismail ismail, Rendy Ismi Hayati Nabila Iwan Stia Budi Joko Triloka Julian Tohir, Oxana Kartika Sari Dewi Ketut Artaye Kusmawaty, Dewi Lailany, Afyra Ar’bah Ma'ruf, Singgih Yulizar Maidaswar Maidaswar Mariana, Tiwuk Mega Fatimah Rosana Mieke Rahayu Miranda, Thesa Moelyono, Achmad Mohamad Fahmi Hafidz Muflikha, Ikha Muhammad Haris Hermanto Muhammad Rama Dhika Muhammad Redintan Justin Mukaromah, Hafsah Mutiara, Suci Nabil Ahyan Annakhief Ni Made Novia Rezkianti Ningsiah Nurdiyanto, Heri Pangesti, Audilla Sekar pratama, rinaldi satria Putri Rafli Banu Satrio Rahmansyah, Ferdian Rahmawati, Dewi Cantika Regina Susanti, Lanny Rendi Saputra Rini Mutahar Rio Kurniawan Rionaldi Ali Rismada, Yessindah Citra Raya Rodiatun, Rodiatun Rofa, Laili Alnur Romadhona, Prima Juanita Rosmita Nuzuliana, Rosmita Ruki Rizal Nul Fikri Runi Amanda Amalia RZ Abdul Aziz Sabur, Ambuy Safira, Zahra Santosa, Bagus Sari, Kiki Yulia Satrio, Rafli Banu Sekar Dana Chiatra Siswahyudianto Siti Rahmawati Sriyanto Suci Fithriya Suhendro Suhendro Yusuf Irianto Sulyono, Sulyono Sushanty Saleh Swandika, I Komang Syahfira, Welda Sylvia Sylvia Tomas, Mary Jane L. Toro, Robby tri utami, septiana Tupari Uichol Kim Vivi Rulviana Wahib, Moh Widi Nugroho, Handoyo Widianti, Evani Putri William Jonathan, William Winda Rika Lestari Wulandari, Hanny Y, M Ariza Eka Y. Suhendro Yan Aditiya Pratama Yufi Wiyos Rini Masykuroh Yulianti, Tantia Alif Yuliatun, Ismiyati Yulmaini Yulmaini Yuni Puspita Sari Yunita Wulandari Yusuf Irianto, Suhendro Zarnelly Zarnelly